Recent Advances in AI-enabled Automated Medical Diagnosis  book cover
1st Edition

Recent Advances in AI-enabled Automated Medical Diagnosis

  • Available for pre-order. Item will ship after October 20, 2022
ISBN 9781032008431
October 20, 2022 Forthcoming by CRC Press
370 Pages 14 Color & 142 B/W Illustrations

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Book Description

Developments in deep learning in the past decade have led to phenomenal growth in AI-based automated medical diagnosis, opening a door to a new era of both medical research and medical industry. It is a golden age for researchers involved in the development and application of advanced machine learning techniques for medical and clinical problems. This book captures the most recent important advances in this cross-disciplinary topic and brings the latest advances to a wide audience including experts, researchers, students, industry developers and medical services.

Table of Contents

Enhancement of COVID-19 Diagnosis using Machine Learning and Texture Analyses of Lung Imaging

Bhuvan Mittal and Junghwan Oh

Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives

Hua-Liang Wei and Steve Billings

Deep Learning based Respiratory Anomaly and COVID Diagnosis Using Audio and CT Scan Imagery

Conor Wall, Chengyu Liu and Li Zhang

COVID-19 forecasting in India through Deep Learning Models

Arindam Chaudhuri and Soumya Ghosh

Deep Learning based Techniques for COVID-19 Diagnostic: A Survey

Fozia Mehboob, Abdul Rauf, Khalid M. Malik, Richard Jiang, Abdul K. J. Saudagar, Abdullah AlTameem and Mohammed AlKhathami

Embedding Explainable Artificial Intelligence in Clinical Decision Support Systems: The Brain Age Prediction Case Study

Angela Lombardi, Domenico Diacono, Nicola Amoroso, Alfonso Monaco, Sabina Tangaro and Roberto Bellotti

Machine Learning based Biological Ageing Estimation: A Survey

Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot

Review on Social Behaviour Analysis of Laboratory Animals: from Methodologies to Applications

Ziping Jiang, Paul Chazot and Richard Jiang

Acute Lymphoblastic Leukemia Diagnosis Using Genetic Algorithm and Enhanced Clustering based Feature Selection

Siew Chin Neoh, Srisukkham Worawut, Li Zhang and Md. Mostafa Kamal Sarker

Artificial Intelligence Enabled Automated Medical Prediction and Diagnosis in Trauma Patients

Lianyong Li, Changqing Zhong, Gang Wang, Wei Wu, Yuzhu Guo, Zheng Zhang, Bo Yang, Xiaotong Lou, Ke Li and Heming Yang

DCGAN Based Facial Expression Synthesis for Emotion Well-being Monitoring with Feature Extraction and Cluster Grouping

Eaby Kollonoor Babu, Kamlesh Mistry and Li Zhang

A Hybrid-DE for Automatic Retinal Image-Based Blood Vessel Segmentation

Colin Paul Joy, Kamlesh Mistry, Gobind Pillai and Li Zhang

Accurate Detection and Analysis of Freezing of Gait in Parkinson’s Disease

Wenting Yang, Simeng Li, Debin Huang, Hantao Li, Lipeng Wang, Yanzhao Wei, Wei Zhang and Yuzhu Guo

Sparse Model Identification for Nonstationary and Nonlinear Neural Dynamics Based on Multiwavelet Basis Expansion

Song Xu, Lina Wang and Jingjing Liu

How Weather Conditions Affect the Spread of Covid-19: Findings from a Study Using Contrastive Learning and NARMAX Models

Yiming Sun and Hua-Liang Wei

New Measurement of the Body Mass Index with Bioimpedance Using a Novel Interpretable Takagi-Sugeno Fuzzy NARX Predictive Model

Changjiang He, Yuanlin Gu, Hua-Liang Wei and Qinggang Meng

Training therapy with BCI-based neurofeedback systems for motor rehabilitation

Qiying Cheng, Jingjing Luo, Hongbo Wang, Qiang Du and Youhao Wang

A Modified Dynamic Time Warping (MDTW) and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings

Xinxin Yao and Hua-Liang Wei

An Investigation on ECG-based Cardiological Diagnosis via Deep Learning Models

Alex Meehan, Zhaonian Zhang, Bryan Williams and Richard Jiang

EEG-based Deep Emotional Diagnosis: A Comparative Study

Geyi Liu, Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot

A Novel Motor Imagery EEG Classification Approach Based on Time-Frequency analysis and Convolutional Neural Network

Qinghua Wang, Lina Wang and Song Xu

Classification of EEG Signals for Brain-Computer Interfaces using a Bayesian-Fuzzy Extreme Learning Machine

Adrian Rubio-Solis, Carlos Beltran-Perez and Wei Hua-Liang

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Richard Jiang is a Senior Lecturer (Associate Professor) in the School of Computing & Communications at Lancaster University, UK. He holds a Leverhulme Trust Research Fellowship. He is a Fellow of HEA, an Associate Member of EPSRC College, and an EPSRC RISE Connector.

Dr Jiang's principal research interests are in Artificial Intelligence, XAI, Biometrics, Privacy & Security, Intelligent Systems, and Biomedical Image Analysis. His recent research has been supported by grants from EPSRC (EP/P009727/1), Leverhulme Trust (RF-2019-492), Qatar National Research Fund (NPRP No.8–140-2–065) and other industry/international funders. He has supervised and co-supervised several PhD students. He authored over 80 publications and was the lead editor of three books published by Springer. He served as a TPC/Editorial member and a reviewer for various international conferences and journals.

Li Zhang is a Reader in Department of Computer Science, Royal Holloway, University of London, UK. She received a PhD degree from the University of Birmingham, UK. She has expertise in machine learning, deep learning, computer vision, and intelligent robotics.

Dr Zhang’s recent research has been funded by Innovate UK (2017-2019, 2018-2020, 2020-2021), Research England (2020-2021), and European Commission (2012-2016, 2014-2018, 2018-2021, 2019-2022). She is the lead Guest Editor for Complexity, Springer Multimedia Tools and Applications and Elsevier Pattern Recognition Letters. She has supervised and co-supervised several PhD students. Dr Zhang is a Senior Member of IEEE.

Hua-Liang Wei is a Senior Lecturer (Associate Professor) with the Department of Automatic Control and Systems Engineering (ACSE), the University of Sheffield, Sheffield, UK. He is head of the Digital Medicine & Computational Neuroscience (DMCN), head of the laboratory of Dynamic Modelling, Data Mining and Decision Making (3DM), and a key member of the Insigneo Institute for in silico medicine and the Neuroscience Institute of the University of Sheffield. He has been awarded grants (as PI and Co-I) from EPSRC, NERC, Norwegian Research Council, EU H2020, Ryder Briggs Charity, and the Royal Society. He received the Ph.D. degree in the Department of Automatic Control and Systems Engineering, the University of Sheffield, UK, in 2004. He has published more than 120 papers (Google Scholar H index 34 and almost 3800 citations) since the completion of the PhD in 2004.

Dr Wei’s research interests include nonlinear system identification, machine learning, neural networks, interpretable machine learning, deep learning, computational intelligence, AI and its applications, data-driven modelling, data mining, pattern recognition, data based and data informed predictive modelling, with applications in medical research and AI-assisted diagnosis among other areas.

Danny Crookes is an Emeritus Professor at Queen’s University Belfast (QUB), where was earlier the Head of the computer science department from 1993 to 2002 and was also appointed to the Chair of the Computer Engineering. He was also the Director of the Research for Speech, Image, and Vision Systems at the Institute for Electronics, Communications, and Information Technology (ECIT), QUB before he retired, in 2017. He is currently an Emeritus Professor in computer science with QUB. He has published more than 200 scientific articles in journals and international conferences, and has presented tutorials on parallel image processing at several international conferences. His current research interests include the use of novel architectures (especially GPUs) for high-performance image processing.

Professor Crookes has applied expertise in language design, optimizing compilers and software generators, plus software tools for hardware description, and architecture generation, to the goal of developing high level software tools to enable rapid development of real-time video processing systems. He has been currently involved in projects in automatic shoeprint recognition (ESPRC), speech separation and enhancement (EPSRC), and processing of 4D confocal microscopy imagery (sponsored by INI and Andor Technologies).

Paul Chazot is an Associate Professor of Neuropharmacology in Biosciences, Durham University. He is leading a research group focusing on the identification, characterisation and validation of novel drug targets for the treatment of the major acute and chronic CNS pathologies. He also dedicates to developing novel all-in-one behavioural tests for both animals and humans. Dr Chazot has developed two new clinical development programmes over the last 20 years, one for chronic pain (Votucalis), and one for Alzheimer’s Disease (PBM-T 1068nm), the former at the pipeline stage for the company Akari Therapeutics UK, and the latter reaching the FDA Phase 2b clinical stage in the US. He is also the Durham lead for the Durham University spin-out company Nevrargenics, who has developed exciting new rational drug leads (RAR-M) for disease-modifying in a range of neurodegenerative diseases.

Dr Chazot is the Past President of European Histamine Research Society, Fellow of British Pharmacological Society, Chair of an International Union of Pharmacological Societies subcommittee, President of Parkinson’s UK Durham Branch, member of Newcastle ARUK funding committee, and Director of the Durham Wolfson Research Institute of Health and Wellbeing Pain Special Interest Group (Northern Pain Alliance) & Enlighten Delirium (recently secured an Innovate UK health device grant), since 2012.